code: def train_step(
      train_state: TrainState,
      batch: dataset.Batch,
  ) -> tuple[TrainState, Scalars]:
      """Applies an update to parameters and returns new state."""
      params, state, opt_state, loss_scale = train_state
      grads, (loss, new_state) = jax.grad(loss_fn, has_aux=True)(
          params, state, loss_scale, batch
      )
  
      # Grads are in "param_dtype" (likely F32) here. We cast them back to the
      # compute dtype such that we do the all-reduce below in the compute precision
      # (which is typically lower than the param precision).
      policy = get_policy()
      grads = policy.cast_to_compute(grads)
      grads = loss_scale.unscale(grads)
  
      # Taking the mean across all replicas to keep params in sync.
      grads = jax.lax.pmean(grads, axis_name="i")
  
      # We compute our optimizer update in the same precision as params, even when
      # doing mixed precision training.
      grads = policy.cast_to_param(grads)
  
      # Compute and apply updates via our optimizer.
      updates, new_opt_state = make_optimizer().update(grads, opt_state)
      new_params = optax.apply_updates(params, updates)
  
      if FLAGS.mp_skip_nonfinite:
          grads_finite = jmp.all_finite(grads)
          loss_scale = loss_scale.adjust(grads_finite)
          new_params, new_state, new_opt_state = jmp.select_tree(
              grads_finite,
              (new_params, new_state, new_opt_state),
              (params, state, opt_state),
          )
  
      # Scalars to log (note: we log the mean across all hosts/devices).
      scalars = {
          "train_loss": loss,
          "loss_scale": loss_scale.loss_scale,
      }
      if FLAGS.mp_skip_nonfinite:
          scalars["grads_finite"] = grads_finite
      new_state, scalars = jmp.cast_to_full((new_state, scalars))
      scalars = jax.lax.pmean(scalars, axis_name="i")
      train_state = TrainState(
          new_params, new_state, new_opt_state, loss_scale
      )
      return train_state, scalars
  
imports: from collections.abc import Iterable, Mapping
  import contextlib
  import functools
  import timeit
  from typing import NamedTuple
  from absl import app
  from absl import flags
  from absl import logging
  import haiku
  from examples.imagenet import dataset
  import jax
  import jax.numpy
  import jmp
  import numpy
  import optax
  import tree
label_desc: Use image node_id 7 for calling a global function with example usage: train_step(train_state, batch) and returns: train_state,  scalars